In policy transfer a previously learned policy is used to learn the new policy. One way to achieve this is by policy distillation [1]}, which means that the agent will select an action by minimizing the divergence of action distributions between the `teacher'(source) policies and the `student'(target) policy. Another approach is probabilistic policy reuse [2]}, where the agent can select an action based on the pre-learned policy instead of his own policy. For example, [3]} use this to teach a humanoid robot how to walk fast by exploiting a policy that allows the robot to walk in a normal speed.
